Load libraries

library(Seurat)
library(princurve)
library(Revelio)
library(monocle)
library(gprofiler2)
library(seriation)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

#Set ggplot theme as classic
theme_set(theme_classic())

Load the full dataset

WT <- readRDS("../QC.filtered.clustered.cells.RDS")
KO <- readRDS("./GmncKO.cells.RDS") %>% subset(idents = c(6:9), invert = T)
DimPlot(object = KO,
        group.by = "Cell.ident",
        reduction = "spring",
        cols = c( "#4cabdc", "#7293c8", "#3ca73f",
                  "#31b6bd","#a9961b", "#ebcb2e", "#9ec22f", 
                  "#e7823a"),
        pt.size = 0.5)  & NoAxes()

Expression of MCC and stress response in WT and KO

MCC.genes <- list(c("Trp73", "Gmnc", "Foxj1", "Myb", "Ccno", "Ccdc67", "Deup1","Mcidas",
                    "E2f4", "E2f5", "Ahr", "Trrap", "Cdc20b", "Ccdc78", "Rfx2",
                    "Rfx3", "Foxn4", "Fank1", "Jazf1", "Ccna1", "Nek10", "Plk4",
                    "Cep63", "Cep152", "Sass6", "Pcnt", "Pcm1", "Cetn2", "Tfdp1"))

KO <- AddModuleScore(KO,
                     features = MCC.genes,
                     name = "MCC_score")

WT <- AddModuleScore(WT,
                     features = MCC.genes,
                     name = "MCC_score")
WT.CR.goterm <- read.table("../CajalRetzius_trajectory/CR_GO_res-by-waves.csv", sep = ";", header = T)

DNA_damage_GOterm <- WT.CR.goterm %>% filter(term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571",
                                                            "GO:0006974", "GO:0006977","GO:0033554",
                                                            "GO:0044773", "GO:0042771", "GO:0042770",
                                                            "GO:2001021", "GO:1902229")
                                             )

DNA_damage_genes <- DNA_damage_GOterm %>%
                    filter(query %in% c("Clust.2", "Clust.3", "Clust.4")) %>%
                    filter(term_id == "GO:0033554") %>%
                    pull(intersection) %>% strsplit("\\,") %>% unlist() %>% unique()

KO <- AddModuleScore(KO,
                     features = list(DNA_damage_genes),
                     name = "cellular_response_to_stress_score")

WT <- AddModuleScore(WT,
                     features = list(DNA_damage_genes),
                     name = "cellular_response_to_stress_score")
gradient <- rev(brewer.pal(8,"RdYlBu"))
lim <-  c(-0.5,0.8)

p1 <- ggplot(KO@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=MCC_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Multiciliation score')


p2 <- ggplot(WT@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=MCC_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Multiciliation score')

p3 <- ggplot(KO@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=cellular_response_to_stress_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Stress response score')

p4 <- ggplot(WT@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=cellular_response_to_stress_score1), size=1, shape=16) + 
  scale_color_gradientn(colours= gradient,
                        limits = lim,
                        name='Stress response score')


MCC.scores.plot <- p2 + p1 
Stress.score.plot <- p4 + p3

for (i in 1:2){
  MCC.scores.plot[[i]]$data <- MCC.scores.plot[[i]]$data[order(MCC.scores.plot[[i]]$data$MCC_score1),]
}

for (i in 1:2){
  Stress.score.plot[[i]]$data <- Stress.score.plot[[i]]$data[order(Stress.score.plot[[i]]$data$cellular_response_to_stress_score1),]
}

MCC.scores.plot / Stress.score.plot

# Compute differentiation states scores

AP

APgenes <- c("Rgcc", "Sparc", "Hes5","Hes1", "Slc1a3",
             "Ddah1", "Ldha", "Hmga2","Sfrp1", "Id4",
             "Creb5", "Ptn", "Lpar1", "Rcn1","Zfp36l1",
             "Sox9", "Sox2", "Nr2e1", "Ttyh1", "Trip6")

KO <- AddModuleScore(KO,
                     features = list(APgenes),
                     name = "AP_signature")

BP

BPgenes <- c("Eomes", "Igsf8", "Insm1", "Elavl2", "Elavl4",
             "Hes6","Gadd45g", "Neurog2", "Btg2", "Neurog1")

KO <- AddModuleScore(KO,
                     features = list(BPgenes),
                     name = "BP_signature")

EN

ENgenes <- c("Mfap4", "Nhlh2", "Nhlh1", "Ppp1r14a", "Nav1",
             "Neurod1", "Sorl1", "Svip", "Cxcl12", "Tenm4",
             "Dll3", "Rgmb", "Cntn2", "Vat1")

KO <- AddModuleScore(KO,
                     features = list(ENgenes),
                     name = "EN_signature")

LN

LNgenes <- c("Snhg11", "Pcsk1n", "Mapt", "Ina", "Stmn4",
             "Gap43", "Tubb2a", "Ly6h","Ptprd", "Mef2c")

KO <- AddModuleScore(KO,
                     features = list(LNgenes),
                     name = "LN_signature")
FeaturePlot(object = KO,
            features = c("AP_signature1", "BP_signature1",
                              "EN_signature1", "LN_signature1"),
            pt.size = 0.75,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

Fit pseudotime on CR and CP differentiating neurons

Group cells in Pallial or CR lineage

KO$Lineage <- sapply(KO$Cell.ident,
                              FUN = function(x) {
                                if (x %in% c("Neuron_prob.2", "Hem")) {
                                  x = "Cajal-Retzius_neurons"
                                } else if (x %in% c("Neuron_prob.3", "Medial_pallium")) {
                                  x = "Pallial_neurons"
                                } else {
                                  x = "other"
                                  }
                              })
DimPlot(KO,
        reduction = "spring",
        group.by = "Lineage",
        pt.size = 1,
        cols =  c("#cc391b","#969696","#026c9a")
        ) + NoAxes()

Fit principale curve on the two lineages

Neurons.data <-  subset(KO,  subset = Lineage %in% c("Cajal-Retzius_neurons", "Pallial_neurons") & Cell.ident %in% c("Neuron_prob.2", "Neuron_prob.3"))

DimPlot(Neurons.data ,
        reduction = "spring",
        group.by = "Lineage",
        pt.size = 1,
        cols =  c("#cc391b","#026c9a")
        ) + NoAxes()

fit <- principal_curve(as.matrix(Neurons.data@meta.data[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = 1,
                       stretch=0)
## Starting curve---distance^2: 76242448164
## Iteration 1---distance^2: 48308567
## Iteration 2---distance^2: 49237931
## Iteration 3---distance^2: 50119026
## Iteration 4---distance^2: 51105768
## Iteration 5---distance^2: 51819999
## Iteration 6---distance^2: 52369574
## Iteration 7---distance^2: 52732675
## Iteration 8---distance^2: 52936995
## Iteration 9---distance^2: 53060989
## Iteration 10---distance^2: 53117718
#Pseudotime score
PseudotimeScore <- fit$lambda/max(fit$lambda)

if (cor(PseudotimeScore, Neurons.data@assays$SCT@data['Hmga2', ]) > 0) {
  Neurons.data$PseudotimeScore <- -(PseudotimeScore - max(PseudotimeScore))
}

cols <- brewer.pal(n =11, name = "Spectral")

ggplot(Neurons.data@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=PseudotimeScore), size=2, shape=16) + 
  scale_color_gradientn(colours=rev(cols), name='Pseudotime score')

Plot pan-neuronal genes along this axis

Neurons.data <- NormalizeData(Neurons.data, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")
Trajectories.neurons <- Neurons.data@meta.data %>% select(Barcodes, Spring_1, Spring_2,
                                                          AP_signature1, BP_signature1, EN_signature1, LN_signature1,
                                                          Lineage, PseudotimeScore)

# Neurog2
p1 <- FeaturePlot(object = Neurons.data,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Neurog2 <- Neurons.data@assays$RNA@data["Neurog2", Trajectories.neurons$Barcodes]

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()
Trajectories.neurons$Tbr1 <- Neurons.data@assays$RNA@data["Tbr1", Trajectories.neurons$Barcodes]

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Mapt <- Neurons.data@assays$RNA@data["Mapt", Trajectories.neurons$Barcodes]

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)

## Shift pseudotime score

score <- sapply(Trajectories.neurons$PseudotimeScore,
                FUN = function(x) if (x <= 0.15) {x= 0.15} else { x=x })

Trajectories.neurons$PseudotimeScore.shifted <- (score - min(score)) / (max(score) - min(score))
# Neurog2
p1 <- FeaturePlot(object = Neurons.data ,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)

Trajectories.neurons$nUMI <- Neurons.data$nCount_RNA

ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

Fit cell cycle trajectory on progenitors

To balance the number of progenitors in both domain we will only work with Hem and Medial_pallium annotated cells. Since we are using pallial cell to contrast CR specific trajectory we think this approximation will not significantly affect our analysis.

Progenitors.data <-  subset(KO,  subset = Lineage %in% c("Cajal-Retzius_neurons", "Pallial_neurons") & Cell.ident %in% c("Hem", "Medial_pallium"))

DimPlot(Progenitors.data,
        reduction = "spring",
        group.by = "Cell.ident",
        pt.size = 1,
        cols =  c("#cc391b","#026c9a")
        ) + NoAxes()

table(Progenitors.data$Cell.ident)
## 
##            Hem Medial_pallium 
##           1114           2983
rm(list = ls()[!ls() %in% c("Trajectories.neurons", "Progenitors.data")])
gc()
##             used  (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells   3460150 184.8    6183287  330.3    6183287  330.3
## Vcells 102114313 779.1  821731051 6269.4 1024259792 7814.5

Prepare data for Revelio

rawCounts <- as.matrix(Progenitors.data[["RNA"]]@counts)

# Filter genes expressed by less than 10 cells
num.cells <- Matrix::rowSums(rawCounts > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
rawCounts <- rawCounts[genes.use, ]

Run Revelio

CCgenes <- read.table("../ChoroidPlexus_trajectory/CCgenes.csv", sep = ";", header = T)

We can now follow the tutorial form the package github page

myData <- createRevelioObject(rawData = rawCounts,
                              cyclicGenes = CCgenes,
                              lowernGeneCutoff = 0,
                              uppernUMICutoff = Inf,
                              ccPhaseAssignBasedOnIndividualBatches = F)
## 2022-05-03 17:19:15: reading data: 6.01secs
rm("rawCounts")
gc()
##             used   (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells   3466368  185.2    6183287  330.3    6183287  330.3
## Vcells 165274580 1261.0  657384841 5015.5 1024259792 7814.5

The getCellCyclePhaseAssignInformation filter “outlier” cells for cell cycle phase assignation. We modified the function to keep all cells as we observed this does not affect the global cell cycle fitting procedure

source("../Functions/functions_InitializationCCPhaseAssignFiltering.R")

myData <- getCellCyclePhaseAssign_allcells(myData)
## 2022-05-03 17:19:22: assigning cell cycle phases: 33.67secs
myData <- getPCAData(dataList = myData)
## 2022-05-03 17:19:55: calculating PCA: 26.65secs
myData <- getOptimalRotation(dataList = myData)
## 2022-05-03 17:20:22: calculating optimal rotation: 14.66secs

Graphical assesment of cell cycle fitting

CellCycledata <- cbind(as.data.frame(t(myData@transformedData$dc$data[1:2,])),
                       nUMI= myData@cellInfo$nUMI,
                       Revelio.phase = factor(myData@cellInfo$ccPhase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1")),
                       Revelio.cc= myData@cellInfo$ccPercentageUniformlySpaced,
                       Domain= Progenitors.data$Cell.ident)
p1 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Revelio.phase), size= 0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5]))

p2 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Domain), size = 0.5) +
        scale_color_manual(values= c("#cc391b","#026c9a"))

p3 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color=Revelio.cc), size=0.5, shape=16) + 
        scale_color_gradientn(colours=rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
                              name='Revelio_cc')

p4 <- ggplot(CellCycledata, aes(x= Revelio.cc, y= nUMI/10000)) +
        geom_point(aes(color= Revelio.phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

(p1 + p2) /(p3 + p4)

Progenitors.data$Revelio.phase <- CellCycledata$Revelio.phase
Progenitors.data$Revelio.cc <- CellCycledata$Revelio.cc

p1 <- FeaturePlot(object = Progenitors.data,
                  features = "Revelio.cc",
                  pt.size = 1,
                  cols = rev(brewer.pal(10,"Spectral")),
                  reduction = "spring",
                  order = T) & NoAxes()

p2 <- DimPlot(object = Progenitors.data,
              group.by = "Revelio.phase",
              pt.size = 1,
              reduction = "spring",
              cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 / p2

Transfert learn cell cycle axis

Progenitors <- Progenitors.data@meta.data %>% select(Barcodes, Spring_1, Spring_2,
                                                     AP_signature1, BP_signature1, EN_signature1, LN_signature1,
                                                     Lineage)

Progenitors$PseudotimeScore <- CellCycledata$Revelio.cc
Progenitors$nUMI <- Progenitors.data$nCount_RNA

Combine Progenitors and differentiating neurons data

# Start with neurons data
Trajectories.all <- Trajectories.neurons %>% select(Barcodes, nUMI, Spring_1, Spring_2, AP_signature1, BP_signature1, EN_signature1, LN_signature1, Lineage)

Trajectories.all$Pseudotime <- Trajectories.neurons$PseudotimeScore.shifted + 0.5
Trajectories.all$Phase <- NA
# Add progenitors data
Trajectories.progenitors <- Progenitors %>%
                              select(Barcodes, nUMI, Spring_1, Spring_2, AP_signature1, BP_signature1, EN_signature1, LN_signature1, Lineage) %>% 
                              mutate(Pseudotime= Progenitors.data$Revelio.cc/2,
                                     Phase = Progenitors.data$Revelio.phase)
Trajectories.all <- rbind(Trajectories.all, Trajectories.progenitors)

Trajectories.all$Phase <- factor(Trajectories.all$Phase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1"))
p1 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color=Pseudotime), size=0.5) + 
        scale_color_gradientn(colours=rev(brewer.pal(n =11, name = "Spectral")), name='Pseudotime score')

p2 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a"))

p1 + p2

p1 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p2 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p1 / p2

p1 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= AP_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")


p2 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= BP_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")

p3 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= EN_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")

p4 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= LN_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")


p1 / p2 / p3 / p4

Subset the full Seurat object

KO <- readRDS("./GmncKO.cells.RDS") %>% subset(idents = c(6:9), invert = T)
Neuro.trajectories <- CreateSeuratObject(counts = KO@assays$RNA@data[, Trajectories.all$Barcodes],
                                         meta.data = Trajectories.all)

spring <- as.matrix(Neuro.trajectories@meta.data %>% select("Spring_1", "Spring_2"))
  
Neuro.trajectories[["spring"]] <- CreateDimReducObject(embeddings = spring, key = "Spring_", assay = DefaultAssay(Neuro.trajectories))
p1 <- FeaturePlot(object = Neuro.trajectories,
            features = "Pseudotime",
            pt.size = 0.5,
            cols = rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- DimPlot(object = Neuro.trajectories,
        group.by = "Lineage",
        pt.size = 0.5,
        reduction = "spring",
        cols =  c("#cc391b", "#026c9a")) & NoAxes()


p3 <- DimPlot(object = Neuro.trajectories,
        group.by = "Phase",
        pt.size = 0.5,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 + p2 + p3

rm(list = ls()[!ls() %in% c("Neuro.trajectories")])
gc()
##            used  (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells  3508900 187.4    6183287  330.3    6183287  330.3
## Vcells 35225206 268.8  757448136 5778.9 1024259792 7814.5

Normalization

Neuro.trajectories<- NormalizeData(Neuro.trajectories, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")
Neuro.trajectories <- FindVariableFeatures(Neuro.trajectories, selection.method = "disp", nfeatures = 3000, assay = "RNA")

Plot some genes along pseudotime

source("../Functions/functions_GeneTrends.R")

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gas1","Sox2",
                          "Neurog2", "Btg2",
                          "Tbr1", "Mapt",
                          "Trp73", "Foxg1"))

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gmnc", "Mcidas",
                          "Foxj1", "Trp73",
                          "Lhx1", "Cdkn1a"))

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Mki67", "Top2a",
                          "H2afx", "Cdkn1c"))

Use monocle2 to model gene expression along cycling axis

Initialize a monocle object

# Transfer metadata
meta.data <- data.frame(Barcode= Neuro.trajectories$Barcodes,
                        Lineage= Neuro.trajectories$Lineage,
                        Pseudotime= Neuro.trajectories$Pseudotime,
                        Phase= Neuro.trajectories$Phase)

Annot.data  <- new('AnnotatedDataFrame', data = meta.data)

# Transfer counts data
var.genes <- Neuro.trajectories[["RNA"]]@var.features
count.data = data.frame(gene_short_name = rownames(Neuro.trajectories[["RNA"]]@data[var.genes,]),
                        row.names = rownames(Neuro.trajectories[["RNA"]]@data[var.genes,]))

feature.data <- new('AnnotatedDataFrame', data = count.data)

# Create the CellDataSet object including variable genes only
gbm_cds <- newCellDataSet(Neuro.trajectories[["RNA"]]@counts[var.genes,],
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 0,
                          expressionFamily = negbinomial())
gbm_cds <- estimateSizeFactors(gbm_cds)
gbm_cds <- estimateDispersions(gbm_cds)
gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)
rm(list = ls()[!ls() %in% c("Neuro.trajectories", "gbm_cds", "Gene.Trend", "Plot.Genes.trend")])
gc()
##            used  (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells  3578420 191.2    6183287  330.3    6183287  330.3
## Vcells 71128071 542.7  605958509 4623.1 1024259792 7814.5

Find Pan-neuronal genes

# Split pallial and subpallial cells for gene expression fitting
#Pallial cells
Pallialcells <- Neuro.trajectories@meta.data %>%
                filter(Lineage == "Pallial_neurons") %>%
                pull(Barcodes)

# Cajal-Retzius cells
CRcells <- Neuro.trajectories@meta.data %>%
                   filter(Lineage == "Cajal-Retzius_neurons") %>%
                   pull(Barcodes)
# We filter-out genes detected in less than 200 or 200 cells along Pallial or CR lineages
num.cells <- Matrix::rowSums(Neuro.trajectories@assays$RNA@counts[,Pallialcells] > 0)
Pallial.expressed <- names(x = num.cells[which(x = num.cells >= 200)])

num.cells <- Matrix::rowSums(Neuro.trajectories@assays$RNA@counts[,CRcells] > 0)
CR.expressed <- names(x = num.cells[which(x = num.cells >= 200)])

Input.genes <- rownames(gbm_cds)[rownames(gbm_cds) %in% intersect(Pallial.expressed, CR.expressed)]
Pallial.genes <- differentialGeneTest(gbm_cds[Input.genes, Pallialcells], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 reducedModelFormulaStr = "~1", 
                                                 cores = parallel::detectCores() - 2)

#Filter based on FDR
Pallial.genes.filtered <- Pallial.genes  %>% filter(qval < 1e-3)
CRcells.genes <- differentialGeneTest(gbm_cds[Input.genes, CRcells], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 reducedModelFormulaStr = "~1", 
                                                 cores = parallel::detectCores() - 2)

#Filter based on FDR
CRcells.genes.filtered <- CRcells.genes  %>% filter(qval < 1e-3)
Common.genes <- intersect(Pallial.genes.filtered$gene_short_name, CRcells.genes.filtered$gene_short_name)
# Smooth genes expression along the two trajectories
nPoints <- 300

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime = seq(min(pData(gbm_cds)$Pseudotime), max(pData(gbm_cds)$Pseudotime), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
curve_matrix <- genSmoothCurves(gbm_cds[as.character(Common.genes),],
                                trend_formula = "~sm.ns(Pseudotime, df = 3)*Lineage",
                                relative_expr = TRUE,
                                new_data = new_data,
                                cores= parallel::detectCores() - 2)
# Extract genes with person's cor > 0.6 between the 2 trajectories

Pallial.smoothed <- scale(t(curve_matrix[,c(1:300)]))  #Pallial points
CR.smoothed <- scale(t(curve_matrix[,c(301:600)])) #CR points

mat <- cor(Pallial.smoothed, CR.smoothed, method = "pearson")

Gene.Cor <- diag(mat)
hist(Gene.Cor, breaks = 100)
abline(v=0.8,col=c("blue"))

PanNeuro.genes <- names(Gene.Cor[Gene.Cor > 0.8])
# Order rows using seriation
dst <- as.dist((1-cor(scale(t(curve_matrix[PanNeuro.genes,c(600:301)])), method = "pearson")))
row.ser <- seriate(dst, method ="MDS_angle") #MDS_angle
gene.order <- PanNeuro.genes[get_order(row.ser)]

anno.colors <- list(lineage = c(Pallial="#026c9a",CR="#cc391b"))


pheatmap::pheatmap(curve_matrix[rev(gene.order),
                                c(1:300, 301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_col = data.frame(lineage = rep(c("Pallial","CR"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color =  viridis::viridis(10),
                   breaks = seq(-2.5,2.5, length.out = 10),
                   main = "")

rm(list = ls()[!ls() %in% c("Neuro.trajectories", "gbm_cds", "Gene.Trend", "Plot.Genes.trend")])
gc()
##            used  (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells  3628460 193.8    7985455  426.5    7985455  426.5
## Vcells 71244938 543.6  387813447 2958.8 1024259792 7814.5

Test each gene trend over pseudotime score

Find genes DE along pseudomaturation axis

pseudo.maturation.diff <- differentialGeneTest(gbm_cds[fData(gbm_cds)$num_cells_expressed > 80,], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)*Lineage", 
                                                 reducedModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 cores = parallel::detectCores() - 2)
# Filter genes based on FDR
pseudo.maturation.diff.filtered <- pseudo.maturation.diff %>% filter(qval < 1e-40)

Direction of the DEG by calculating the area between curves (ABC)

Smooth commun genes along the two trajectories

# Create a new pseudo-DV vector of 300 points
nPoints <- 300

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime = seq(min(pData(gbm_cds)$Pseudotime), max(pData(gbm_cds)$Pseudotime), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
Diff.curve_matrix <- genSmoothCurves(gbm_cds[as.character(pseudo.maturation.diff.filtered$gene_short_name),],
                                      trend_formula = "~sm.ns(Pseudotime, df = 3)*Lineage",
                                      relative_expr = TRUE,
                                      new_data = new_data,
                                      cores= parallel::detectCores() - 2)

Compute the ABC for each gene

# Extract matrix containing smoothed curves for each lineages
Pal_curve_matrix <- Diff.curve_matrix[, 1:nPoints] #Pallial points
CR_curve_matrix <- Diff.curve_matrix[, (nPoints + 1):(2 * nPoints)] #CR points

# Direction of the comparison : postive ABCs <=> Upregulated in CR lineage
ABCs_res <- CR_curve_matrix - Pal_curve_matrix

# Average logFC between the 2 curves
ILR_res <- log2(CR_curve_matrix/ (Pal_curve_matrix + 0.1)) 
  
ABCs_res <- apply(ABCs_res, 1, function(x, nPoints) {
                  avg_delta_x <- (x[1:(nPoints - 1)] + x[2:(nPoints)])/2
                  step <- (100/(nPoints - 1))
                  res <- round(sum(avg_delta_x * step), 3)
                  return(res)},
                  nPoints = nPoints) # Compute the area below the curve
  
ABCs_res <- cbind(ABCs_res, ILR_res[,ncol(ILR_res)])
colnames(ABCs_res)<- c("ABCs", "Endpoint_ILR")

# Import ABC values into the DE test results table
pseudo.maturation.diff.filtered <- cbind(pseudo.maturation.diff.filtered[,1:4],
                                         ABCs_res,
                                         pseudo.maturation.diff.filtered[,5:6])

Cajal-Retzius cells specific trajectory analysis

# Extract Cajal-Retzius expressed genes
CR.res <- as.data.frame(pseudo.maturation.diff.filtered[pseudo.maturation.diff.filtered$ABCs > 0,])
CR.genes <- row.names(CR.res)

CR_curve_matrix <- CR_curve_matrix[CR.genes, ]

Gene expression profiles along the trajectory

## Cluster gene by expression profiles
Pseudotime.genes.clusters <- cluster::pam(as.dist((1 - cor(Matrix::t(CR_curve_matrix),method = "pearson"))), k= 5)

CR.Gene.dynamique <- data.frame(Gene= names(Pseudotime.genes.clusters$clustering),
                                 Waves= Pseudotime.genes.clusters$clustering,
                                 Gene.Clusters = Pseudotime.genes.clusters$clustering,
                                 q.val = CR.res$qval,
                                 ABCs= CR.res$ABCs
                                 ) %>% arrange(Gene.Clusters)

row.names(CR.Gene.dynamique) <- CR.Gene.dynamique$Gene
CR.Gene.dynamique$Gene.Clusters <- paste0("Clust.", CR.Gene.dynamique$Gene.Clusters)
# Order the rows using seriation
dst <- as.dist((1-cor(scale(t(CR_curve_matrix)), method = "pearson")))
row.ser <- seriation::seriate(dst, method ="R2E") #"R2E" #TSP #"GW" "GW_ward"
gene.order <- rownames(CR_curve_matrix[get_order(row.ser),])

# Set annotation colors
pal <- wes_palette("Darjeeling1")
anno.colors <- list(lineage = c(Pallial_neurons="#026c9a", Cajal_Retzius="#cc391b"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal 
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

We manually correct the reordering so genes are aligned from top left to bottom rigth

pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal 
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

anno.colors <- list(Cell.state = c(Cycling_RG="#046c9a", Differentiating_cells="#ebcb2e"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))

col.anno <- data.frame(Cell.state = rep(c("Cycling_RG","Differentiating_cells"), c(100,200)))
rownames(col.anno) <- 301:600

pheatmap::pheatmap(CR_curve_matrix[gene.order,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = col.anno,
                   annotation_colors = anno.colors,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

diff.state <- Neuro.trajectories@meta.data %>%
              filter(Lineage ==  "Cajal-Retzius_neurons") %>%
              select("AP_signature1", "BP_signature1", "EN_signature1", "LN_signature1", "Pseudotime")

AP.loess <- loess(AP_signature1 ~ Pseudotime, diff.state)
AP.smooth <- predict(AP.loess,
                     seq(0.01,1.5, length.out= 300))

BP.loess <- loess(BP_signature1 ~ Pseudotime, diff.state)
BP.smooth <- predict(BP.loess,
                     seq(0.01,1.5, length.out= 300))

EN.loess <- loess(EN_signature1 ~ Pseudotime, diff.state)
EN.smooth <- predict(EN.loess,
                     seq(0.01,1.5, length.out= 300))

LN.loess <- loess(LN_signature1 ~ Pseudotime, diff.state)
LN.smooth <- predict(LN.loess,
                     seq(0.01,1.5, length.out= 300))

Smoothed.states <- cbind(AP.smooth, BP.smooth, EN.smooth, LN.smooth)
heatmap.states <- pheatmap::pheatmap(as.data.frame(t(Smoothed.states)),
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   gaps_col = 100,
                   gaps_row = c(1,2,3),
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  rev(colorRampPalette(brewer.pal(n= 8, name = "RdBu"))(100)),
                   breaks = seq(-1,1, length.out = 100),
                   main = "")
heatmap.gene <- pheatmap::pheatmap(CR_curve_matrix[gene.order,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
cowplot::plot_grid(heatmap.states$gtable, heatmap.gene$gtable,
                   ncol = 1,
                   align = "v",
                   rel_heights = c(1,3),
                   greedy = T)

Gene cluster trend

source("../Functions/functions_GeneClusterTrend.R")

Plot.clust.trends(Neuro.trajectories,
                   Lineage = "Cajal-Retzius_neurons",
                   Which.cluster = 1:5,
                   clust.list = Pseudotime.genes.clusters$clustering,
                   Smooth.method = "gam")

GO term enrichment in gene clusters using gprofiler2

CR.gostres <- gost(query = list("Clust.1" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.1") %>% pull(Gene) %>% as.character(),
                             "Clust.2" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.2") %>% pull(Gene) %>% as.character(),
                             "Clust.3" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.3") %>% pull(Gene) %>% as.character(),
                             "Clust.4" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.4") %>% pull(Gene) %>% as.character(),
                             "Clust.5" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.5") %>% pull(Gene) %>% as.character()),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR.gostres$result,2,as.character),
            "KO_CR_GO_res-by-waves.csv", sep = ";", quote = F, row.names = F)
DNA_damage_GOterm <- CR.gostres$result[CR.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977","GO:0033554",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(9,1,2,3,5,6,7,11)]
## # A tibble: 0 × 8
## # … with 8 variables: term_id <chr>, query <chr>, significant <lgl>,
## #   p_value <dbl>, query_size <int>, intersection_size <int>, precision <dbl>,
## #   term_name <chr>

Go term on all CR genes

CR.gostres <- gost(query = as.character(CR.Gene.dynamique$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR.gostres$result,2,as.character),
            "KOCR_GO_res.csv", sep = ";", quote = F, row.names = F)
DNA_damage_GOterm <- CR.gostres$result[CR.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
## # A tibble: 0 × 7
## # … with 7 variables: query <chr>, significant <lgl>, p_value <dbl>,
## #   query_size <int>, intersection_size <int>, precision <dbl>, term_name <chr>

Intersection with ChP dynamicaly expressed genes

ChP_dynamic_genes <- read.table("../ChoroidPlexus_trajectory/ChP.Gene.dynamique.csv", sep = ";", header = T, row.names = 1)
CR_ChP_common_genes <- CR.Gene.dynamique %>% filter(Gene %in% ChP_dynamic_genes$Gene)
gene.order2 <- gene.order[gene.order %in% CR_ChP_common_genes$Gene]

pheatmap::pheatmap(CR_curve_matrix[gene.order2,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   #annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   #annotation_col = col.anno,
                   #annotation_colors = anno.colors,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

CR_ChP_common.gostres <- gost(query = list("Clust.1" = CR_ChP_common_genes %>% filter(Gene.Clusters == "Clust.1") %>% pull(Gene) %>% as.character(),
                             "Clust.2" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.2") %>% pull(Gene) %>% as.character(),
                             "Clust.3" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.3") %>% pull(Gene) %>% as.character(),
                             "Clust.4" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.4") %>% pull(Gene) %>% as.character(),
                             "Clust.5" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.5") %>% pull(Gene) %>% as.character()),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR_ChP_common.gostres$result,2,as.character),
            "KO_CR_ChP_common_GO_res-by-waves.csv", sep = ";", quote = F, row.names = F)
DNA_damage_GOterm <- CR_ChP_common.gostres$result[CR_ChP_common.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                       "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
## # A tibble: 0 × 7
## # … with 7 variables: query <chr>, significant <lgl>, p_value <dbl>,
## #   query_size <int>, intersection_size <int>, precision <dbl>, term_name <chr>
CR_ChP_common.gostres <- gost(query = as.character(CR_ChP_common_genes$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR_ChP_common.gostres$result,2,as.character),
            "KO_CR_ChP_common_GO_res_all.csv", sep = ";", quote = F, row.names = F)

Pallial neurons trajectory analysis

# Extract Pallial neurons trajectory genes
Pal.res <- as.data.frame(pseudo.maturation.diff.filtered[pseudo.maturation.diff.filtered$ABCs < 0,])
Pal.genes <- row.names(Pal.res)

Pal_curve_matrix <- Pal_curve_matrix[Pal.genes, ]

Gene expression profiles along the trajectory

## Cluster gene by expression profiles
Pseudotime.genes.clusters <- cluster::pam(as.dist((1 - cor(Matrix::t(Pal_curve_matrix),method = "pearson"))), k= 5)

Pal.Gene.dynamique <- data.frame(Gene= names(Pseudotime.genes.clusters$clustering),
                             Waves= Pseudotime.genes.clusters$clustering,
                             Gene.Clusters = Pseudotime.genes.clusters$clustering,
                             q.val = Pal.res$pval,
                             ABCs= Pal.res$ABCs
                             ) %>% arrange(Gene.Clusters)

row.names(Pal.Gene.dynamique) <- Pal.Gene.dynamique$Gene
Pal.Gene.dynamique$Gene.Clusters <- paste0("Clust.", Pal.Gene.dynamique$Gene.Clusters)
# Order the rows using seriation
dst <- as.dist((1-cor(scale(t(Pal_curve_matrix)), method = "pearson")))
row.ser <- seriation::seriate(dst, method ="R2E") #"R2E" #TSP #"GW" "GW_ward"
gene.order <- rownames(Pal_curve_matrix[get_order(row.ser),])

# Set annotation colors
pal <- wes_palette("Darjeeling1")
anno.colors <- list(lineage = c(Pallial_neurons="#026c9a", Cajal_Retzius="#cc391b"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

We manually correct the reordering so genes are aligned from top right to bottom left

pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

anno.colors <- list(Cell.state = c(Cycling_RG="#046c9a", Differentiating_cells="#ebcb2e"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))

col.anno <- data.frame(Cell.state = rep(c("Differentiating_cells","Cycling_RG"), c(200,100)))
rownames(col.anno) <- 300:1

pheatmap::pheatmap(Pal_curve_matrix[gene.order,300:1],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = col.anno,
                   annotation_colors = anno.colors,
                   gaps_col = 200,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

Gene cluster trend

Plot.clust.trends(Neuro.trajectories,
                   Lineage = "Pallial_neurons",
                   Which.cluster = 1:5,
                   clust.list = Pseudotime.genes.clusters$clustering,
                   Smooth.method = "gam")

Pal.gostres <- gost(query = as.character(Pal.Gene.dynamique$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(Pal.gostres$result, 2, as.character),
            "KO_Pal.gostres.csv", sep = ";", quote = F, row.names = F)
DNA_damage_GOterm <- Pal.gostres$result[Pal.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
## # A tibble: 7 × 7
##   query   significant p_value query_size intersection_size precision term_name  
##   <chr>   <lgl>         <dbl>      <int>             <int>     <dbl> <chr>      
## 1 query_1 TRUE        0.00461        160                 3    0.0188 mitotic G1…
## 2 query_1 TRUE        0.00649        160                 4    0.025  DNA damage…
## 3 query_1 TRUE        0.0132         160                 3    0.0188 regulation…
## 4 query_1 TRUE        0.0207         160                 3    0.0188 intrinsic …
## 5 query_1 TRUE        0.0211         160                 2    0.0125 DNA damage…
## 6 query_1 TRUE        0.0287         160                 5    0.0312 signal tra…
## 7 query_1 TRUE        0.0358         160                 4    0.025  intrinsic …

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")
## [1] "03 mai, 2022, 17,44"
#Packages used
sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] splines   stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6   cowplot_1.1.1       ggExtra_0.9        
##  [4] RColorBrewer_1.1-2  dplyr_1.0.7         seriation_1.3.1    
##  [7] gprofiler2_0.2.1    monocle_2.22.0      DDRTree_0.1.5      
## [10] irlba_2.3.3         VGAM_1.1-5          ggplot2_3.3.5      
## [13] Biobase_2.54.0      BiocGenerics_0.40.0 Matrix_1.4-1       
## [16] Revelio_0.1.0       princurve_2.1.6     SeuratObject_4.0.4 
## [19] Seurat_4.0.5       
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.6            igraph_1.2.11         lazyeval_0.2.2       
##   [4] densityClust_0.3      listenv_0.8.0         scattermore_0.7      
##   [7] fastICA_1.2-3         digest_0.6.29         foreach_1.5.1        
##  [10] htmltools_0.5.2       viridis_0.6.2         fansi_0.5.0          
##  [13] magrittr_2.0.2        tensor_1.5            cluster_2.1.3        
##  [16] ROCR_1.0-11           limma_3.50.0          globals_0.14.0       
##  [19] matrixStats_0.61.0    docopt_0.7.1          spatstat.sparse_2.0-0
##  [22] colorspace_2.0-2      ggrepel_0.9.1         xfun_0.28            
##  [25] RCurl_1.98-1.5        sparsesvd_0.2         crayon_1.4.2         
##  [28] jsonlite_1.7.2        spatstat.data_2.1-0   survival_3.2-13      
##  [31] zoo_1.8-9             iterators_1.0.13      glue_1.5.1           
##  [34] polyclip_1.10-0       registry_0.5-1        gtable_0.3.0         
##  [37] leiden_0.3.9          future.apply_1.8.1    abind_1.4-5          
##  [40] scales_1.1.1          pheatmap_1.0.12       DBI_1.1.1            
##  [43] miniUI_0.1.1.1        Rcpp_1.0.8            viridisLite_0.4.0    
##  [46] xtable_1.8-4          reticulate_1.22       spatstat.core_2.3-1  
##  [49] htmlwidgets_1.5.4     httr_1.4.2            FNN_1.1.3            
##  [52] ellipsis_0.3.2        ica_1.0-2             farver_2.1.0         
##  [55] pkgconfig_2.0.3       sass_0.4.0            uwot_0.1.10          
##  [58] deldir_1.0-6          utf8_1.2.2            labeling_0.4.2       
##  [61] tidyselect_1.1.1      rlang_0.4.12          reshape2_1.4.4       
##  [64] later_1.3.0           munsell_0.5.0         tools_4.1.3          
##  [67] cli_3.1.0             generics_0.1.1        ggridges_0.5.3       
##  [70] evaluate_0.14         stringr_1.4.0         fastmap_1.1.0        
##  [73] yaml_2.2.1            goftest_1.2-3         knitr_1.36           
##  [76] fitdistrplus_1.1-6    purrr_0.3.4           RANN_2.6.1           
##  [79] pbapply_1.5-0         future_1.23.0         nlme_3.1-153         
##  [82] mime_0.12             slam_0.1-49           rstudioapi_0.13      
##  [85] compiler_4.1.3        plotly_4.10.0         png_0.1-7            
##  [88] spatstat.utils_2.2-0  tibble_3.1.6          bslib_0.3.1          
##  [91] stringi_1.7.6         highr_0.9             lattice_0.20-45      
##  [94] HSMMSingleCell_1.14.0 vctrs_0.3.8           pillar_1.6.4         
##  [97] lifecycle_1.0.1       spatstat.geom_2.3-0   combinat_0.0-8       
## [100] lmtest_0.9-39         jquerylib_0.1.4       RcppAnnoy_0.0.19     
## [103] bitops_1.0-7          data.table_1.14.2     httpuv_1.6.3         
## [106] patchwork_1.1.1       R6_2.5.1              promises_1.2.0.1     
## [109] TSP_1.1-11            KernSmooth_2.23-20    gridExtra_2.3        
## [112] parallelly_1.29.0     codetools_0.2-18      MASS_7.3-56          
## [115] assertthat_0.2.1      withr_2.4.3           qlcMatrix_0.9.7      
## [118] sctransform_0.3.2     mgcv_1.8-40           parallel_4.1.3       
## [121] grid_4.1.3            rpart_4.1.16          tidyr_1.1.4          
## [124] rmarkdown_2.11        Rtsne_0.15            shiny_1.7.1

  1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, ↩︎

---
title: "Cajal-Retzius cells Trajectory"
author:
   - Matthieu Moreau^[Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr] [![](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0002-2592-2373)
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_document: 
    code_download: yes
    df_print: tibble
    highlight: haddock
    theme: cosmo
    css: "../style.css"
    toc: yes
    toc_depth: 5
    toc_float:
      collapsed: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.align = 'center', message=FALSE, warning=FALSE, cache.lazy = FALSE)

# To use biomart 
new_config <- httr::config(ssl_verifypeer = FALSE)
httr::set_config(new_config, override = FALSE)
```

# Load libraries

```{r message=FALSE, warning=FALSE}
library(Seurat)
library(princurve)
library(Revelio)
library(monocle)
library(gprofiler2)
library(seriation)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

#Set ggplot theme as classic
theme_set(theme_classic())
```

# Load the full dataset

```{r}
WT <- readRDS("../QC.filtered.clustered.cells.RDS")
KO <- readRDS("./GmncKO.cells.RDS") %>% subset(idents = c(6:9), invert = T)
```

```{r}
DimPlot(object = KO,
        group.by = "Cell.ident",
        reduction = "spring",
        cols = c( "#4cabdc", "#7293c8", "#3ca73f",
                  "#31b6bd","#a9961b", "#ebcb2e", "#9ec22f", 
                  "#e7823a"),
        pt.size = 0.5)  & NoAxes()
```

# Expression of MCC and stress response in WT and KO

```{r}
MCC.genes <- list(c("Trp73", "Gmnc", "Foxj1", "Myb", "Ccno", "Ccdc67", "Deup1","Mcidas",
                    "E2f4", "E2f5", "Ahr", "Trrap", "Cdc20b", "Ccdc78", "Rfx2",
                    "Rfx3", "Foxn4", "Fank1", "Jazf1", "Ccna1", "Nek10", "Plk4",
                    "Cep63", "Cep152", "Sass6", "Pcnt", "Pcm1", "Cetn2", "Tfdp1"))

KO <- AddModuleScore(KO,
                     features = MCC.genes,
                     name = "MCC_score")

WT <- AddModuleScore(WT,
                     features = MCC.genes,
                     name = "MCC_score")
```

```{r}
WT.CR.goterm <- read.table("../CajalRetzius_trajectory/CR_GO_res-by-waves.csv", sep = ";", header = T)

DNA_damage_GOterm <- WT.CR.goterm %>% filter(term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571",
                                                            "GO:0006974", "GO:0006977","GO:0033554",
                                                            "GO:0044773", "GO:0042771", "GO:0042770",
                                                            "GO:2001021", "GO:1902229")
                                             )

DNA_damage_genes <- DNA_damage_GOterm %>%
                    filter(query %in% c("Clust.2", "Clust.3", "Clust.4")) %>%
                    filter(term_id == "GO:0033554") %>%
                    pull(intersection) %>% strsplit("\\,") %>% unlist() %>% unique()

KO <- AddModuleScore(KO,
                     features = list(DNA_damage_genes),
                     name = "cellular_response_to_stress_score")

WT <- AddModuleScore(WT,
                     features = list(DNA_damage_genes),
                     name = "cellular_response_to_stress_score")
```


```{r}
gradient <- rev(brewer.pal(8,"RdYlBu"))
lim <-  c(-0.5,0.8)

p1 <- ggplot(KO@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=MCC_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Multiciliation score')


p2 <- ggplot(WT@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=MCC_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Multiciliation score')

p3 <- ggplot(KO@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=cellular_response_to_stress_score1), size=1, shape=16) + 
  scale_color_gradientn(colours=gradient,
                        limits = lim,
                        name='Stress response score')

p4 <- ggplot(WT@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=cellular_response_to_stress_score1), size=1, shape=16) + 
  scale_color_gradientn(colours= gradient,
                        limits = lim,
                        name='Stress response score')


MCC.scores.plot <- p2 + p1 
Stress.score.plot <- p4 + p3

for (i in 1:2){
  MCC.scores.plot[[i]]$data <- MCC.scores.plot[[i]]$data[order(MCC.scores.plot[[i]]$data$MCC_score1),]
}

for (i in 1:2){
  Stress.score.plot[[i]]$data <- Stress.score.plot[[i]]$data[order(Stress.score.plot[[i]]$data$cellular_response_to_stress_score1),]
}

MCC.scores.plot / Stress.score.plot
```
# Compute differentiation states scores

## AP

```{r}
APgenes <- c("Rgcc", "Sparc", "Hes5","Hes1", "Slc1a3",
             "Ddah1", "Ldha", "Hmga2","Sfrp1", "Id4",
             "Creb5", "Ptn", "Lpar1", "Rcn1","Zfp36l1",
             "Sox9", "Sox2", "Nr2e1", "Ttyh1", "Trip6")

KO <- AddModuleScore(KO,
                     features = list(APgenes),
                     name = "AP_signature")
```

## BP

```{r}
BPgenes <- c("Eomes", "Igsf8", "Insm1", "Elavl2", "Elavl4",
             "Hes6","Gadd45g", "Neurog2", "Btg2", "Neurog1")

KO <- AddModuleScore(KO,
                     features = list(BPgenes),
                     name = "BP_signature")
```

## EN

```{r}
ENgenes <- c("Mfap4", "Nhlh2", "Nhlh1", "Ppp1r14a", "Nav1",
             "Neurod1", "Sorl1", "Svip", "Cxcl12", "Tenm4",
             "Dll3", "Rgmb", "Cntn2", "Vat1")

KO <- AddModuleScore(KO,
                     features = list(ENgenes),
                     name = "EN_signature")
```

## LN

```{r}
LNgenes <- c("Snhg11", "Pcsk1n", "Mapt", "Ina", "Stmn4",
             "Gap43", "Tubb2a", "Ly6h","Ptprd", "Mef2c")

KO <- AddModuleScore(KO,
                     features = list(LNgenes),
                     name = "LN_signature")
```

```{r}
FeaturePlot(object = KO,
            features = c("AP_signature1", "BP_signature1",
                              "EN_signature1", "LN_signature1"),
            pt.size = 0.75,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()
```

# Fit pseudotime on CR and CP differentiating neurons

## Group cells in Pallial or CR lineage

```{r}
KO$Lineage <- sapply(KO$Cell.ident,
                              FUN = function(x) {
                                if (x %in% c("Neuron_prob.2", "Hem")) {
                                  x = "Cajal-Retzius_neurons"
                                } else if (x %in% c("Neuron_prob.3", "Medial_pallium")) {
                                  x = "Pallial_neurons"
                                } else {
                                  x = "other"
                                  }
                              })
```

```{r}
DimPlot(KO,
        reduction = "spring",
        group.by = "Lineage",
        pt.size = 1,
        cols =  c("#cc391b","#969696","#026c9a")
        ) + NoAxes()
```


## Fit principale curve on the two lineages

```{r}
Neurons.data <-  subset(KO,  subset = Lineage %in% c("Cajal-Retzius_neurons", "Pallial_neurons") & Cell.ident %in% c("Neuron_prob.2", "Neuron_prob.3"))

DimPlot(Neurons.data ,
        reduction = "spring",
        group.by = "Lineage",
        pt.size = 1,
        cols =  c("#cc391b","#026c9a")
        ) + NoAxes()
```

```{r}
fit <- principal_curve(as.matrix(Neurons.data@meta.data[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = 1,
                       stretch=0)
```

```{r}
#Pseudotime score
PseudotimeScore <- fit$lambda/max(fit$lambda)

if (cor(PseudotimeScore, Neurons.data@assays$SCT@data['Hmga2', ]) > 0) {
  Neurons.data$PseudotimeScore <- -(PseudotimeScore - max(PseudotimeScore))
}

cols <- brewer.pal(n =11, name = "Spectral")

ggplot(Neurons.data@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=PseudotimeScore), size=2, shape=16) + 
  scale_color_gradientn(colours=rev(cols), name='Pseudotime score')
```

## Plot pan-neuronal genes along this axis

```{r}
Neurons.data <- NormalizeData(Neurons.data, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")
```

```{r fig.dim=c(9,10)}
Trajectories.neurons <- Neurons.data@meta.data %>% select(Barcodes, Spring_1, Spring_2,
                                                          AP_signature1, BP_signature1, EN_signature1, LN_signature1,
                                                          Lineage, PseudotimeScore)

# Neurog2
p1 <- FeaturePlot(object = Neurons.data,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Neurog2 <- Neurons.data@assays$RNA@data["Neurog2", Trajectories.neurons$Barcodes]

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()
Trajectories.neurons$Tbr1 <- Neurons.data@assays$RNA@data["Tbr1", Trajectories.neurons$Barcodes]

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Mapt <- Neurons.data@assays$RNA@data["Mapt", Trajectories.neurons$Barcodes]

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)
```
## Shift pseudotime score

```{r}
score <- sapply(Trajectories.neurons$PseudotimeScore,
                FUN = function(x) if (x <= 0.15) {x= 0.15} else { x=x })

Trajectories.neurons$PseudotimeScore.shifted <- (score - min(score)) / (max(score) - min(score))
```

```{r}
# Neurog2
p1 <- FeaturePlot(object = Neurons.data ,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)
```


```{r}
Trajectories.neurons$nUMI <- Neurons.data$nCount_RNA

ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)
```

# Fit cell cycle trajectory on progenitors

To balance the number of progenitors in both domain we will only work with *Hem* and *Medial_pallium* annotated cells. Since we are using pallial cell to contrast CR specific trajectory we think this approximation will not significantly affect our analysis.

```{r}
Progenitors.data <-  subset(KO,  subset = Lineage %in% c("Cajal-Retzius_neurons", "Pallial_neurons") & Cell.ident %in% c("Hem", "Medial_pallium"))

DimPlot(Progenitors.data,
        reduction = "spring",
        group.by = "Cell.ident",
        pt.size = 1,
        cols =  c("#cc391b","#026c9a")
        ) + NoAxes()
```
```{r}
table(Progenitors.data$Cell.ident)
```

```{r}
rm(list = ls()[!ls() %in% c("Trajectories.neurons", "Progenitors.data")])
gc()
```

## Prepare data for Revelio

```{r}
rawCounts <- as.matrix(Progenitors.data[["RNA"]]@counts)

# Filter genes expressed by less than 10 cells
num.cells <- Matrix::rowSums(rawCounts > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
rawCounts <- rawCounts[genes.use, ]
```

## Run Revelio

```{r}
CCgenes <- read.table("../ChoroidPlexus_trajectory/CCgenes.csv", sep = ";", header = T)
```

We can now follow the tutorial form the [package github page](https://github.com/danielschw188/Revelio)


```{r}
myData <- createRevelioObject(rawData = rawCounts,
                              cyclicGenes = CCgenes,
                              lowernGeneCutoff = 0,
                              uppernUMICutoff = Inf,
                              ccPhaseAssignBasedOnIndividualBatches = F)

rm("rawCounts")
gc()
```
The getCellCyclePhaseAssignInformation filter “outlier” cells for cell cycle phase assignation. We modified the function to keep all cells as we observed this does not affect the global cell cycle fitting procedure

```{r}
source("../Functions/functions_InitializationCCPhaseAssignFiltering.R")

myData <- getCellCyclePhaseAssign_allcells(myData)
```

```{r}
myData <- getPCAData(dataList = myData)

myData <- getOptimalRotation(dataList = myData)
```
## Graphical assesment of cell cycle fitting

```{r}
CellCycledata <- cbind(as.data.frame(t(myData@transformedData$dc$data[1:2,])),
                       nUMI= myData@cellInfo$nUMI,
                       Revelio.phase = factor(myData@cellInfo$ccPhase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1")),
                       Revelio.cc= myData@cellInfo$ccPercentageUniformlySpaced,
                       Domain= Progenitors.data$Cell.ident)
```

```{r}
p1 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Revelio.phase), size= 0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5]))

p2 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Domain), size = 0.5) +
        scale_color_manual(values= c("#cc391b","#026c9a"))

p3 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color=Revelio.cc), size=0.5, shape=16) + 
        scale_color_gradientn(colours=rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
                              name='Revelio_cc')

p4 <- ggplot(CellCycledata, aes(x= Revelio.cc, y= nUMI/10000)) +
        geom_point(aes(color= Revelio.phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

(p1 + p2) /(p3 + p4)
```
```{r}
Progenitors.data$Revelio.phase <- CellCycledata$Revelio.phase
Progenitors.data$Revelio.cc <- CellCycledata$Revelio.cc

p1 <- FeaturePlot(object = Progenitors.data,
                  features = "Revelio.cc",
                  pt.size = 1,
                  cols = rev(brewer.pal(10,"Spectral")),
                  reduction = "spring",
                  order = T) & NoAxes()

p2 <- DimPlot(object = Progenitors.data,
              group.by = "Revelio.phase",
              pt.size = 1,
              reduction = "spring",
              cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 / p2
```

## Transfert learn cell cycle axis

```{r}
Progenitors <- Progenitors.data@meta.data %>% select(Barcodes, Spring_1, Spring_2,
                                                     AP_signature1, BP_signature1, EN_signature1, LN_signature1,
                                                     Lineage)

Progenitors$PseudotimeScore <- CellCycledata$Revelio.cc
Progenitors$nUMI <- Progenitors.data$nCount_RNA
```

# Combine Progenitors and differentiating neurons data

```{r}
# Start with neurons data
Trajectories.all <- Trajectories.neurons %>% select(Barcodes, nUMI, Spring_1, Spring_2, AP_signature1, BP_signature1, EN_signature1, LN_signature1, Lineage)

Trajectories.all$Pseudotime <- Trajectories.neurons$PseudotimeScore.shifted + 0.5
Trajectories.all$Phase <- NA
```

```{r}
# Add progenitors data
Trajectories.progenitors <- Progenitors %>%
                              select(Barcodes, nUMI, Spring_1, Spring_2, AP_signature1, BP_signature1, EN_signature1, LN_signature1, Lineage) %>% 
                              mutate(Pseudotime= Progenitors.data$Revelio.cc/2,
                                     Phase = Progenitors.data$Revelio.phase)
```

```{r}
Trajectories.all <- rbind(Trajectories.all, Trajectories.progenitors)

Trajectories.all$Phase <- factor(Trajectories.all$Phase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1"))
```

```{r}
p1 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color=Pseudotime), size=0.5) + 
        scale_color_gradientn(colours=rev(brewer.pal(n =11, name = "Spectral")), name='Pseudotime score')

p2 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a"))

p1 + p2
```
```{r}
p1 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p2 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p1 / p2
```


```{r}
p1 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= AP_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")


p2 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= BP_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")

p3 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= EN_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")

p4 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= LN_signature1)) +
  geom_point(aes(color= Lineage), size=0.5) +
  scale_color_manual(values= c("#cc391b", "#026c9a")) +
  geom_smooth(aes(color= Lineage), method="loess", n= 50, fill="grey")


p1 / p2 / p3 / p4
```

## Subset the full Seurat object

```{r}
KO <- readRDS("./GmncKO.cells.RDS") %>% subset(idents = c(6:9), invert = T)
```

```{r}
Neuro.trajectories <- CreateSeuratObject(counts = KO@assays$RNA@data[, Trajectories.all$Barcodes],
                                         meta.data = Trajectories.all)

spring <- as.matrix(Neuro.trajectories@meta.data %>% select("Spring_1", "Spring_2"))
  
Neuro.trajectories[["spring"]] <- CreateDimReducObject(embeddings = spring, key = "Spring_", assay = DefaultAssay(Neuro.trajectories))
```

```{r}
p1 <- FeaturePlot(object = Neuro.trajectories,
            features = "Pseudotime",
            pt.size = 0.5,
            cols = rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- DimPlot(object = Neuro.trajectories,
        group.by = "Lineage",
        pt.size = 0.5,
        reduction = "spring",
        cols =  c("#cc391b", "#026c9a")) & NoAxes()


p3 <- DimPlot(object = Neuro.trajectories,
        group.by = "Phase",
        pt.size = 0.5,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 + p2 + p3
```
```{r}
rm(list = ls()[!ls() %in% c("Neuro.trajectories")])
gc()
```

## Normalization

```{r}
Neuro.trajectories<- NormalizeData(Neuro.trajectories, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")
```

```{r}
Neuro.trajectories <- FindVariableFeatures(Neuro.trajectories, selection.method = "disp", nfeatures = 3000, assay = "RNA")
```

## Plot some genes along pseudotime

```{r fig.dim=c(9,8)}
source("../Functions/functions_GeneTrends.R")

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gas1","Sox2",
                          "Neurog2", "Btg2",
                          "Tbr1", "Mapt",
                          "Trp73", "Foxg1"))
```

```{r fig.dim=c(9,6)}
Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gmnc", "Mcidas",
                          "Foxj1", "Trp73",
                          "Lhx1", "Cdkn1a"))
```

```{r fig.dim=c(9,5)}
Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Mki67", "Top2a",
                          "H2afx", "Cdkn1c"))
```

# Use monocle2 to model gene expression along cycling axis

## Initialize a monocle object

```{r}
# Transfer metadata
meta.data <- data.frame(Barcode= Neuro.trajectories$Barcodes,
                        Lineage= Neuro.trajectories$Lineage,
                        Pseudotime= Neuro.trajectories$Pseudotime,
                        Phase= Neuro.trajectories$Phase)

Annot.data  <- new('AnnotatedDataFrame', data = meta.data)

# Transfer counts data
var.genes <- Neuro.trajectories[["RNA"]]@var.features
count.data = data.frame(gene_short_name = rownames(Neuro.trajectories[["RNA"]]@data[var.genes,]),
                        row.names = rownames(Neuro.trajectories[["RNA"]]@data[var.genes,]))

feature.data <- new('AnnotatedDataFrame', data = count.data)

# Create the CellDataSet object including variable genes only
gbm_cds <- newCellDataSet(Neuro.trajectories[["RNA"]]@counts[var.genes,],
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 0,
                          expressionFamily = negbinomial())
```

```{r message=FALSE, warning=FALSE}
gbm_cds <- estimateSizeFactors(gbm_cds)
gbm_cds <- estimateDispersions(gbm_cds)
gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)
```

```{r}
rm(list = ls()[!ls() %in% c("Neuro.trajectories", "gbm_cds", "Gene.Trend", "Plot.Genes.trend")])
gc()
```
## Find Pan-neuronal genes

```{r}
# Split pallial and subpallial cells for gene expression fitting
#Pallial cells
Pallialcells <- Neuro.trajectories@meta.data %>%
                filter(Lineage == "Pallial_neurons") %>%
                pull(Barcodes)

# Cajal-Retzius cells
CRcells <- Neuro.trajectories@meta.data %>%
                   filter(Lineage == "Cajal-Retzius_neurons") %>%
                   pull(Barcodes)
```

```{r}
# We filter-out genes detected in less than 200 or 200 cells along Pallial or CR lineages
num.cells <- Matrix::rowSums(Neuro.trajectories@assays$RNA@counts[,Pallialcells] > 0)
Pallial.expressed <- names(x = num.cells[which(x = num.cells >= 200)])

num.cells <- Matrix::rowSums(Neuro.trajectories@assays$RNA@counts[,CRcells] > 0)
CR.expressed <- names(x = num.cells[which(x = num.cells >= 200)])

Input.genes <- rownames(gbm_cds)[rownames(gbm_cds) %in% intersect(Pallial.expressed, CR.expressed)]
```


```{r  message=FALSE, warning=FALSE, cache=TRUE}
Pallial.genes <- differentialGeneTest(gbm_cds[Input.genes, Pallialcells], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 reducedModelFormulaStr = "~1", 
                                                 cores = parallel::detectCores() - 2)

#Filter based on FDR
Pallial.genes.filtered <- Pallial.genes  %>% filter(qval < 1e-3)
```

```{r  message=FALSE, warning=FALSE, cache=TRUE}
CRcells.genes <- differentialGeneTest(gbm_cds[Input.genes, CRcells], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 reducedModelFormulaStr = "~1", 
                                                 cores = parallel::detectCores() - 2)

#Filter based on FDR
CRcells.genes.filtered <- CRcells.genes  %>% filter(qval < 1e-3)
```

```{r}
Common.genes <- intersect(Pallial.genes.filtered$gene_short_name, CRcells.genes.filtered$gene_short_name)
```

```{r, cache=TRUE}
# Smooth genes expression along the two trajectories
nPoints <- 300

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime = seq(min(pData(gbm_cds)$Pseudotime), max(pData(gbm_cds)$Pseudotime), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
curve_matrix <- genSmoothCurves(gbm_cds[as.character(Common.genes),],
                                trend_formula = "~sm.ns(Pseudotime, df = 3)*Lineage",
                                relative_expr = TRUE,
                                new_data = new_data,
                                cores= parallel::detectCores() - 2)
```

```{r}
# Extract genes with person's cor > 0.6 between the 2 trajectories

Pallial.smoothed <- scale(t(curve_matrix[,c(1:300)]))  #Pallial points
CR.smoothed <- scale(t(curve_matrix[,c(301:600)])) #CR points

mat <- cor(Pallial.smoothed, CR.smoothed, method = "pearson")

Gene.Cor <- diag(mat)
hist(Gene.Cor, breaks = 100)
abline(v=0.8,col=c("blue"))
```
```{r}
PanNeuro.genes <- names(Gene.Cor[Gene.Cor > 0.8])
```

```{r}
# Order rows using seriation
dst <- as.dist((1-cor(scale(t(curve_matrix[PanNeuro.genes,c(600:301)])), method = "pearson")))
row.ser <- seriate(dst, method ="MDS_angle") #MDS_angle
gene.order <- PanNeuro.genes[get_order(row.ser)]

anno.colors <- list(lineage = c(Pallial="#026c9a",CR="#cc391b"))


pheatmap::pheatmap(curve_matrix[rev(gene.order),
                                c(1:300, 301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_col = data.frame(lineage = rep(c("Pallial","CR"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color =  viridis::viridis(10),
                   breaks = seq(-2.5,2.5, length.out = 10),
                   main = "")
```

```{r}
rm(list = ls()[!ls() %in% c("Neuro.trajectories", "gbm_cds", "Gene.Trend", "Plot.Genes.trend")])
gc()
```

## Test each gene trend over pseudotime score

### Find genes DE along pseudomaturation axis

```{r message=FALSE, warning=FALSE, cache=TRUE}
pseudo.maturation.diff <- differentialGeneTest(gbm_cds[fData(gbm_cds)$num_cells_expressed > 80,], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)*Lineage", 
                                                 reducedModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 cores = parallel::detectCores() - 2)

```

```{r}
# Filter genes based on FDR
pseudo.maturation.diff.filtered <- pseudo.maturation.diff %>% filter(qval < 1e-40)
```

## Direction of the DEG by calculating the area between curves (ABC)

### Smooth commun genes along the two trajectories

```{r Smooth gene expression, message=FALSE, warning=FALSE, cache=TRUE}
# Create a new pseudo-DV vector of 300 points
nPoints <- 300

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime = seq(min(pData(gbm_cds)$Pseudotime), max(pData(gbm_cds)$Pseudotime), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
Diff.curve_matrix <- genSmoothCurves(gbm_cds[as.character(pseudo.maturation.diff.filtered$gene_short_name),],
                                      trend_formula = "~sm.ns(Pseudotime, df = 3)*Lineage",
                                      relative_expr = TRUE,
                                      new_data = new_data,
                                      cores= parallel::detectCores() - 2)
```

### Compute the ABC for each gene

```{r Compute the ABC}
# Extract matrix containing smoothed curves for each lineages
Pal_curve_matrix <- Diff.curve_matrix[, 1:nPoints] #Pallial points
CR_curve_matrix <- Diff.curve_matrix[, (nPoints + 1):(2 * nPoints)] #CR points

# Direction of the comparison : postive ABCs <=> Upregulated in CR lineage
ABCs_res <- CR_curve_matrix - Pal_curve_matrix

# Average logFC between the 2 curves
ILR_res <- log2(CR_curve_matrix/ (Pal_curve_matrix + 0.1)) 
  
ABCs_res <- apply(ABCs_res, 1, function(x, nPoints) {
                  avg_delta_x <- (x[1:(nPoints - 1)] + x[2:(nPoints)])/2
                  step <- (100/(nPoints - 1))
                  res <- round(sum(avg_delta_x * step), 3)
                  return(res)},
                  nPoints = nPoints) # Compute the area below the curve
  
ABCs_res <- cbind(ABCs_res, ILR_res[,ncol(ILR_res)])
colnames(ABCs_res)<- c("ABCs", "Endpoint_ILR")

# Import ABC values into the DE test results table
pseudo.maturation.diff.filtered <- cbind(pseudo.maturation.diff.filtered[,1:4],
                                         ABCs_res,
                                         pseudo.maturation.diff.filtered[,5:6])
```

## Cajal-Retzius cells specific trajectory analysis

```{r}
# Extract Cajal-Retzius expressed genes
CR.res <- as.data.frame(pseudo.maturation.diff.filtered[pseudo.maturation.diff.filtered$ABCs > 0,])
CR.genes <- row.names(CR.res)

CR_curve_matrix <- CR_curve_matrix[CR.genes, ]
```

### Gene expression profiles along the trajectory

```{r}
## Cluster gene by expression profiles
Pseudotime.genes.clusters <- cluster::pam(as.dist((1 - cor(Matrix::t(CR_curve_matrix),method = "pearson"))), k= 5)

CR.Gene.dynamique <- data.frame(Gene= names(Pseudotime.genes.clusters$clustering),
                                 Waves= Pseudotime.genes.clusters$clustering,
                                 Gene.Clusters = Pseudotime.genes.clusters$clustering,
                                 q.val = CR.res$qval,
                                 ABCs= CR.res$ABCs
                                 ) %>% arrange(Gene.Clusters)

row.names(CR.Gene.dynamique) <- CR.Gene.dynamique$Gene
CR.Gene.dynamique$Gene.Clusters <- paste0("Clust.", CR.Gene.dynamique$Gene.Clusters)
```

```{r CR gene heatmap, fig.dim=c(9, 5)}
# Order the rows using seriation
dst <- as.dist((1-cor(scale(t(CR_curve_matrix)), method = "pearson")))
row.ser <- seriation::seriate(dst, method ="R2E") #"R2E" #TSP #"GW" "GW_ward"
gene.order <- rownames(CR_curve_matrix[get_order(row.ser),])

# Set annotation colors
pal <- wes_palette("Darjeeling1")
anno.colors <- list(lineage = c(Pallial_neurons="#026c9a", Cajal_Retzius="#cc391b"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal 
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```

We manually correct the reordering so genes are aligned from top left to bottom rigth

```{r fig.dim=c(9, 5)}
pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal 
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```


```{r fig.dim=c(9, 5)}
anno.colors <- list(Cell.state = c(Cycling_RG="#046c9a", Differentiating_cells="#ebcb2e"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))

col.anno <- data.frame(Cell.state = rep(c("Cycling_RG","Differentiating_cells"), c(100,200)))
rownames(col.anno) <- 301:600

pheatmap::pheatmap(CR_curve_matrix[gene.order,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = col.anno,
                   annotation_colors = anno.colors,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

```

```{r}
diff.state <- Neuro.trajectories@meta.data %>%
              filter(Lineage ==  "Cajal-Retzius_neurons") %>%
              select("AP_signature1", "BP_signature1", "EN_signature1", "LN_signature1", "Pseudotime")

AP.loess <- loess(AP_signature1 ~ Pseudotime, diff.state)
AP.smooth <- predict(AP.loess,
                     seq(0.01,1.5, length.out= 300))

BP.loess <- loess(BP_signature1 ~ Pseudotime, diff.state)
BP.smooth <- predict(BP.loess,
                     seq(0.01,1.5, length.out= 300))

EN.loess <- loess(EN_signature1 ~ Pseudotime, diff.state)
EN.smooth <- predict(EN.loess,
                     seq(0.01,1.5, length.out= 300))

LN.loess <- loess(LN_signature1 ~ Pseudotime, diff.state)
LN.smooth <- predict(LN.loess,
                     seq(0.01,1.5, length.out= 300))

Smoothed.states <- cbind(AP.smooth, BP.smooth, EN.smooth, LN.smooth)
```

```{r, fig.show="hide"}
heatmap.states <- pheatmap::pheatmap(as.data.frame(t(Smoothed.states)),
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   gaps_col = 100,
                   gaps_row = c(1,2,3),
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  rev(colorRampPalette(brewer.pal(n= 8, name = "RdBu"))(100)),
                   breaks = seq(-1,1, length.out = 100),
                   main = "")

heatmap.gene <- pheatmap::pheatmap(CR_curve_matrix[gene.order,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```


```{r}
cowplot::plot_grid(heatmap.states$gtable, heatmap.gene$gtable,
                   ncol = 1,
                   align = "v",
                   rel_heights = c(1,3),
                   greedy = T)
```

### Gene cluster trend

```{r fig.dim=c(9,6), cache=TRUE}
source("../Functions/functions_GeneClusterTrend.R")

Plot.clust.trends(Neuro.trajectories,
                   Lineage = "Cajal-Retzius_neurons",
                   Which.cluster = 1:5,
                   clust.list = Pseudotime.genes.clusters$clustering,
                   Smooth.method = "gam")
```

### GO term enrichment in gene clusters using gprofiler2

```{r}
CR.gostres <- gost(query = list("Clust.1" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.1") %>% pull(Gene) %>% as.character(),
                             "Clust.2" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.2") %>% pull(Gene) %>% as.character(),
                             "Clust.3" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.3") %>% pull(Gene) %>% as.character(),
                             "Clust.4" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.4") %>% pull(Gene) %>% as.character(),
                             "Clust.5" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.5") %>% pull(Gene) %>% as.character()),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR.gostres$result,2,as.character),
            "KO_CR_GO_res-by-waves.csv", sep = ";", quote = F, row.names = F)
```

```{r}
DNA_damage_GOterm <- CR.gostres$result[CR.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977","GO:0033554",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(9,1,2,3,5,6,7,11)]
```

### Go term on all CR genes

```{r}
CR.gostres <- gost(query = as.character(CR.Gene.dynamique$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR.gostres$result,2,as.character),
            "KOCR_GO_res.csv", sep = ";", quote = F, row.names = F)
```


```{r}
DNA_damage_GOterm <- CR.gostres$result[CR.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
```

### Intersection with ChP dynamicaly expressed genes

```{r}
ChP_dynamic_genes <- read.table("../ChoroidPlexus_trajectory/ChP.Gene.dynamique.csv", sep = ";", header = T, row.names = 1)
```


```{r}
CR_ChP_common_genes <- CR.Gene.dynamique %>% filter(Gene %in% ChP_dynamic_genes$Gene)
```

```{r}
gene.order2 <- gene.order[gene.order %in% CR_ChP_common_genes$Gene]

pheatmap::pheatmap(CR_curve_matrix[gene.order2,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   #annotation_row = CR.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   #annotation_col = col.anno,
                   #annotation_colors = anno.colors,
                   gaps_col = 100,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```
```{r}
CR_ChP_common.gostres <- gost(query = list("Clust.1" = CR_ChP_common_genes %>% filter(Gene.Clusters == "Clust.1") %>% pull(Gene) %>% as.character(),
                             "Clust.2" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.2") %>% pull(Gene) %>% as.character(),
                             "Clust.3" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.3") %>% pull(Gene) %>% as.character(),
                             "Clust.4" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.4") %>% pull(Gene) %>% as.character(),
                             "Clust.5" = CR.Gene.dynamique %>% filter(Gene.Clusters == "Clust.5") %>% pull(Gene) %>% as.character()),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR_ChP_common.gostres$result,2,as.character),
            "KO_CR_ChP_common_GO_res-by-waves.csv", sep = ";", quote = F, row.names = F)
```

```{r}
DNA_damage_GOterm <- CR_ChP_common.gostres$result[CR_ChP_common.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                       "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
```

```{r}
CR_ChP_common.gostres <- gost(query = as.character(CR_ChP_common_genes$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(CR_ChP_common.gostres$result,2,as.character),
            "KO_CR_ChP_common_GO_res_all.csv", sep = ";", quote = F, row.names = F)
```


## Pallial neurons trajectory analysis

```{r}
# Extract Pallial neurons trajectory genes
Pal.res <- as.data.frame(pseudo.maturation.diff.filtered[pseudo.maturation.diff.filtered$ABCs < 0,])
Pal.genes <- row.names(Pal.res)

Pal_curve_matrix <- Pal_curve_matrix[Pal.genes, ]
```

### Gene expression profiles along the trajectory

```{r}
## Cluster gene by expression profiles
Pseudotime.genes.clusters <- cluster::pam(as.dist((1 - cor(Matrix::t(Pal_curve_matrix),method = "pearson"))), k= 5)

Pal.Gene.dynamique <- data.frame(Gene= names(Pseudotime.genes.clusters$clustering),
                             Waves= Pseudotime.genes.clusters$clustering,
                             Gene.Clusters = Pseudotime.genes.clusters$clustering,
                             q.val = Pal.res$pval,
                             ABCs= Pal.res$ABCs
                             ) %>% arrange(Gene.Clusters)

row.names(Pal.Gene.dynamique) <- Pal.Gene.dynamique$Gene
Pal.Gene.dynamique$Gene.Clusters <- paste0("Clust.", Pal.Gene.dynamique$Gene.Clusters)
```

```{r fig.dim=c(9, 5)}
# Order the rows using seriation
dst <- as.dist((1-cor(scale(t(Pal_curve_matrix)), method = "pearson")))
row.ser <- seriation::seriate(dst, method ="R2E") #"R2E" #TSP #"GW" "GW_ward"
gene.order <- rownames(Pal_curve_matrix[get_order(row.ser),])

# Set annotation colors
pal <- wes_palette("Darjeeling1")
anno.colors <- list(lineage = c(Pallial_neurons="#026c9a", Cajal_Retzius="#cc391b"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```

We manually correct the reordering so genes are aligned from top right to bottom left

```{r fig.dim=c(9, 5)}
pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(300:1,#Pal
                                  301:600)], #CR
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Pallial_neurons","Cajal_Retzius"), each=300)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")
```

```{r fig.dim=c(9, 5)}
anno.colors <- list(Cell.state = c(Cycling_RG="#046c9a", Differentiating_cells="#ebcb2e"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))

col.anno <- data.frame(Cell.state = rep(c("Differentiating_cells","Cycling_RG"), c(200,100)))
rownames(col.anno) <- 300:1

pheatmap::pheatmap(Pal_curve_matrix[gene.order,300:1],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Pal.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = col.anno,
                   annotation_colors = anno.colors,
                   gaps_col = 200,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

```

### Gene cluster trend

```{r fig.dim=c(9,6), cache=TRUE}
Plot.clust.trends(Neuro.trajectories,
                   Lineage = "Pallial_neurons",
                   Which.cluster = 1:5,
                   clust.list = Pseudotime.genes.clusters$clustering,
                   Smooth.method = "gam")
```

```{r}
Pal.gostres <- gost(query = as.character(Pal.Gene.dynamique$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

write.table(apply(Pal.gostres$result, 2, as.character),
            "KO_Pal.gostres.csv", sep = ";", quote = F, row.names = F)
```


```{r}
DNA_damage_GOterm <- Pal.gostres$result[Pal.gostres$result$term_id %in% c("GO:0008630", "GO:0030330", "GO:0031571", "GO:0006974", "GO:0006977",
                                                                                 "GO:0044773", "GO:0042771", "GO:0042770", "GO:2001021", "GO:1902229"),]

DNA_damage_GOterm[,c(1,2,3,5,6,7,11)]
```


# Session Info

```{r}
#date
format(Sys.time(), "%d %B, %Y, %H,%M")

#Packages used
sessionInfo()
```